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Cre Document Ingestion

skill-ahacker-1-cre-agent-skills-cre-document-ingestion · by ahacker-1

CRE Document Ingestion suite — 4 specialist skills for classifying and extracting structured data from deal documents including rent rolls, T-12 financials, and offering memoranda.

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Install

$ agentstack add skill-ahacker-1-cre-agent-skills-cre-document-ingestion

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

CRE Document Ingestion Suite

You have access to 4 specialist document processing skills for commercial real estate deal packages.

Available Skills

| Skill | File | Use When | |-------|------|----------| | Document Classifier | skills/document-classifier.md | User provides one or more deal documents and needs them identified by type (rent roll, T-12, offering memo, lease, survey, etc.) | | Rent Roll Parser | skills/rent-roll-parser.md | User provides a rent roll file and needs structured data extracted — unit numbers, tenants, lease dates, rents, deposits, status | | Financials Parser | skills/financials-parser.md | User provides a T-12 or operating statement and needs structured extraction — income lines, expense categories, monthly trends | | Offering Memo Parser | skills/offering-memo-parser.md | User provides an offering memorandum and needs key data extracted — property details, financial projections, market data, investment highlights |

How to Use

  1. If the user provides documents without specifying what they are, start with the Document Classifier
  2. Once document types are identified, load the appropriate parser skill
  3. Follow the Strategy steps in the loaded skill exactly
  4. Produce structured output in the format specified by the skill
  5. Run Quality Checks before delivering results

Recommended workflow for a full deal package:

  1. Read skills/document-classifier.md → classify all documents
  2. For each rent roll: Read skills/rent-roll-parser.md → extract
  3. For each T-12/financial: Read skills/financials-parser.md → extract
  4. For each offering memo: Read skills/offering-memo-parser.md → extract

If the user says "$ARGUMENTS", use that to determine which skill to load.

Quick Reference

Document Classifier — Identifies: rent rolls, T-12/T-3 operating statements, offering memoranda, leases, title commitments, surveys, Phase I ESAs, appraisals, insurance certificates, tax returns, entity documents. Outputs: document type, confidence level, extractable data fields.

Rent Roll Parser — Extracts: unit number, unit type, square footage, tenant name, lease start/end, monthly rent, security deposit, unit status, move-in date, concessions. Validates: unit count completeness, rent reasonableness, date consistency.

Financials Parser — Extracts: income line items (rental income, vacancy loss, other income), expense categories (taxes, insurance, utilities, R&M, management, payroll, turnover, admin), monthly and annual totals. Calculates: per-unit metrics, expense ratios, year-over-year trends.

Offering Memo Parser — Extracts: property name/address, unit count/mix, year built, lot size, asking price, in-place NOI, pro forma NOI, cap rate, occupancy, market highlights, seller's financial projections, comparable sales, rent comps.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.